Comparison to Xarray

---

Imports

import matplotlib.pyplot as plt
import uxarray as ux
import xarray as xr

fig_size = 400
plot_kwargs = {"backend": "matplotlib",
               "aspect": 2,
               "fig_size": fig_size}

Data

Structured

base_path = "../../meshfiles/"
ds_path = base_path + "outCSne30.structured.nc"

xrds = xr.open_dataset(ds_path)
xrds

<xarray.Dataset> Size: 30kB
Dimensions:  (lat: 45, lon: 80)
Coordinates:
  * lat      (lat) int64 360B -90 -86 -82 -78 -74 -70 -66 ... 66 70 74 78 82 86
  * lon      (lon) float64 640B -180.0 -175.5 -171.0 ... 166.5 171.0 175.5
Data variables:
    psi      (lat, lon) float64 29kB ...

xarray.Dataset

• Dimensions:
  • lat: 45
  • lon: 80
• Coordinates: (2)
  • lat
    (lat)
    int64
    -90 -86 -82 -78 -74 ... 74 78 82 86

    units :

    degrees_north

    array([-90, -86, -82, -78, -74, -70, -66, -62, -58, -54, -50, -46, -42, -38,
           -34, -30, -26, -22, -18, -14, -10,  -6,  -2,   2,   6,  10,  14,  18,
            22,  26,  30,  34,  38,  42,  46,  50,  54,  58,  62,  66,  70,  74,
            78,  82,  86])

  • lon
    (lon)
    float64
    -180.0 -175.5 ... 171.0 175.5

    units :

    degrees_east

    array([-180. , -175.5, -171. , -166.5, -162. , -157.5, -153. , -148.5, -144. ,
           -139.5, -135. , -130.5, -126. , -121.5, -117. , -112.5, -108. , -103.5,
            -99. ,  -94.5,  -90. ,  -85.5,  -81. ,  -76.5,  -72. ,  -67.5,  -63. ,
            -58.5,  -54. ,  -49.5,  -45. ,  -40.5,  -36. ,  -31.5,  -27. ,  -22.5,
            -18. ,  -13.5,   -9. ,   -4.5,    0. ,    4.5,    9. ,   13.5,   18. ,
             22.5,   27. ,   31.5,   36. ,   40.5,   45. ,   49.5,   54. ,   58.5,
             63. ,   67.5,   72. ,   76.5,   81. ,   85.5,   90. ,   94.5,   99. ,
            103.5,  108. ,  112.5,  117. ,  121.5,  126. ,  130.5,  135. ,  139.5,
            144. ,  148.5,  153. ,  157.5,  162. ,  166.5,  171. ,  175.5])

• Data variables: (1)
  • psi
    (lat, lon)
    float64
    ...

    regrid_method :

    nearest_s2d

    [3600 values with dtype=float64]

• Indexes: (2)
  • lat
    PandasIndex

    PandasIndex(Index([-90, -86, -82, -78, -74, -70, -66, -62, -58, -54, -50, -46, -42, -38,
           -34, -30, -26, -22, -18, -14, -10,  -6,  -2,   2,   6,  10,  14,  18,
            22,  26,  30,  34,  38,  42,  46,  50,  54,  58,  62,  66,  70,  74,
            78,  82,  86],
          dtype='int64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([-180.0, -175.5, -171.0, -166.5, -162.0, -157.5, -153.0, -148.5, -144.0,
           -139.5, -135.0, -130.5, -126.0, -121.5, -117.0, -112.5, -108.0, -103.5,
            -99.0,  -94.5,  -90.0,  -85.5,  -81.0,  -76.5,  -72.0,  -67.5,  -63.0,
            -58.5,  -54.0,  -49.5,  -45.0,  -40.5,  -36.0,  -31.5,  -27.0,  -22.5,
            -18.0,  -13.5,   -9.0,   -4.5,    0.0,    4.5,    9.0,   13.5,   18.0,
             22.5,   27.0,   31.5,   36.0,   40.5,   45.0,   49.5,   54.0,   58.5,
             63.0,   67.5,   72.0,   76.5,   81.0,   85.5,   90.0,   94.5,   99.0,
            103.5,  108.0,  112.5,  117.0,  121.5,  126.0,  130.5,  135.0,  139.5,
            144.0,  148.5,  153.0,  157.5,  162.0,  166.5,  171.0,  175.5],
          dtype='float64', name='lon'))

• Attributes: (0)

Unstructured

base_path = "../../meshfiles/"
grid_filename = base_path + "outCSne30.grid.ug"
data_filename = base_path + "outCSne30.data.nc"

uxds = ux.open_dataset(grid_filename, data_filename)
uxds

<xarray.UxDataset> Size: 43kB
Dimensions:  (n_face: 5400)
Dimensions without coordinates: n_face
Data variables:
    psi      (n_face) float64 43kB 1.351 1.331 1.31 1.289 ... 0.6909 0.67 0.6495

xarray.UxDataset

• Dimensions:
  • n_face: 5400
• Coordinates: (0)
• Data variables: (1)
  • psi
    (n_face)
    float64
    1.351 1.331 1.31 ... 0.67 0.6495

    array([1.35131705, 1.33091472, 1.31013985, ..., 0.69088338, 0.66998883,
           0.64946691])

• Indexes: (0)
• Attributes: (0)

Visualization

Xarray

xrds["psi"].plot(figsize=(12, 5), cmap="inferno")

<matplotlib.collections.QuadMesh at 0x7fca45f100a0>

fig, axs = plt.subplots(nrows=2, figsize=(12, 10))

xrds["psi"].plot(cmap="inferno", ax=axs[0])
xrds["psi"].plot(cmap="cividis", ax=axs[1])

<matplotlib.collections.QuadMesh at 0x7fca44935480>

UXarray

todo

uxds["psi"].plot(width=1000, height=500, cmap="inferno")

Since Xarray’s plotting functionality is written using Matplotlib we can use the matplotlib backend in UXarray to obtain a better match.

Additionally, we can rasterize our polygons instead of plotting them directly.

ADD ADMONATION TO OTHER SECTION

uxds["psi"].plot.rasterize(method='polygon', cmap="inferno", **plot_kwargs)

(
    uxds["psi"].plot.rasterize(method='polygon', cmap="inferno", **plot_kwargs)
    + uxds["psi"].plot.rasterize(method='polygon', cmap="cividis", **plot_kwargs)
).opts(fig_size=fig_size).cols(1)

Xarray with hvPlot

One of the primary drawbacks to UXarray’s use of HoloViews for visualization is that there is no direct way to integrate plots generated with Xarray and UXarray together. This can be alleviated by using the hvPlot library, specifically hvplot.xarray, on Xarray’s data structures.

import holoviews as hv
import hvplot.xarray

hv.extension("bokeh")

By using xrds.hvplot() as opposed to xrds.plot(), we can create a simple figure showcasing our Structured Grid figure from Xarray and Unstructured Grid figure from UXarray in a single plot.

hv.extension("bokeh")
(
    xrds.hvplot(cmap="inferno", title="Xarray with hvPlot", width=1000, height=500)
    + uxds["psi"].plot(cmap="inferno", title="UXarray Plot", width=1000, height=500)
).cols(1)

If we are not interested in the outlines of each polygon in our Unstructured Grid, we can create a raster plot, which will better match the result produced by Xarray.

ADD NOTE ABOUT POLYGON SECTION

hv.extension("matplotlib")
(
    xrds.hvplot(cmap="inferno", title="Xarray with hvPlot", aspect=2)
    + uxds["psi"].plot.rasterize(method='polygon', cmap="inferno", title="UXarray Plot", **plot_kwargs)
).opts(fig_size=fig_size).cols(1)

In addition to using hvPlot, the user could also use HoloViews or Datashader on Xarray’s data structures, but this cookbook will not go into detail.

ADD LINK TO EXAMPLES?